A one health framework to estimate the cost of antimicrobial resistance
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(2020) 9:187
RESEARCH
Open Access
A one health framework to estimate the cost of antimicrobial resistance Chantal M. Morel1, Richard A. Alm2, Christine Årdal3, Alessandra Bandera4,5, Giacomo M. Bruno6,7, Elena Carrara8, Giorgio L. Colombo9, Marlieke E. A. de Kraker10, Sabiha Essack11, Isabel Frost12, Bruno Gonzalez-Zorn13, Herman Goossens14, Luca Guardabassi15, Stephan Harbarth10,16, Peter S. Jørgensen17,18, Souha S. Kanj19, Tomislav Kostyanev14, Ramanan Laxminarayan12, Finola Leonard20, Gabriel Levy Hara21,22, Marc Mendelson23, Malgorzata Mikulska24, Nico T. Mutters25, Kevin Outterson2, Jesus Rodriguez Baňo26,27, Evelina Tacconelli8,28, Luigia Scudeller4,29* and the GAP-ON€ network
Abstract Objectives/purpose: The costs attributable to antimicrobial resistance (AMR) remain theoretical and largely unspecified. Current figures fail to capture the full health and economic burden caused by AMR across human, animal, and environmental health; historically many studies have considered only direct costs associated with human infection from a hospital perspective, primarily from high-income countries. The Global Antimicrobial Resistance Platform for ONE-Burden Estimates (GAP-ON€) network has developed a framework to help guide AMR costing exercises in any part of the world as a first step towards more comprehensive analyses for comparing AMR interventions at the local level as well as more harmonized analyses for quantifying the full economic burden attributable to AMR at the global level. Methods: GAP-ON€ (funded under the JPIAMR 8th call (Virtual Research Institute) is composed of 19 international networks and institutions active in the field of AMR. For this project, the Network operated by means of Delphi rounds, teleconferences and face-to-face meetings. The resulting costing framework takes a bottom-up approach to incorporate all relevant costs imposed by an AMR bacterial microbe in a patient, in an animal, or in the environment up through to the societal level. Results: The framework itemizes the epidemiological data as well as the direct and indirect cost components needed to build a realistic cost picture for AMR. While the framework lists a large number of relevant pathogens for which this framework could be used to explore the costs, the framework is sufficiently generic to facilitate the costing of other resistant pathogens, including those of other aetiologies. Conclusion: In order to conduct cost-effectiveness analyses to choose amongst different AMR-related interventions at local level, the costing of AMR should be done according to local epidemiological priorities and local health service norms. Yet the use of a common framework across settings allows for the results of such studies to contribute to cumulative estimates that can serve as the basis of broader policy decisions at the international level such as how to steer R&D funding and how to prioritize AMR amongst other issues. Indeed, it is only by building a realistic cost picture that we can make informed decisions on how best to tackle major heal
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